LIME Explanations for Two Misclassified Instances

Prosenjit Das published this dataset on figshare in 2026. It contains Local Interpretable Model-agnostic Explanations (LIME) for two specific misclassified instances from an unspecified machine learning model. The data is stored in a 5.5 KB Excel file.

Use Cases

• Debugging model errors based on LIME explanations for specific instances.
• Comparing feature importance for misclassified versus correctly classified data points.
• Teaching or demonstrating the application of LIME on real model outputs.
• Analyzing the local decision boundaries of a model around failure cases.

Strengths

• Data is openly licensed under CC-BY-4.0.
• File size is 5.5 KB, making it quick to download and inspect.
• Focuses on specific, indexed instances (73 and 176), providing a concrete case study.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

figshare, author Prosenjit Das.

Collection Method

Likely generated by applying the LIME algorithm to a model's predictions.

Freshness

Last updated 2026-06-01 17:51:46; freshness should be verified.